Statistical Sciences Group

Repeated measures degradation studies are used to assess product or component reliability when there are few or even no failures expected during a study. Such studies are used to assess the shelf life of materials and products. We show how to evaluate the properties of proposed test plans needed to identify statistically efficient tests. We consider test plans for applications where parameters related to the degradation distribution or the lifetime distribution are to be estimated. We use the approximate large-sample variance-covariance matrix of the parameters of a mixed effects linear regression model for repeated measures degradation data to assess the effect of sample size (number of units and number of measurements within the units) on estimation precision of both degradation and failure-time distribution quantiles. We also illustrate the complementary use of simulation-based methods for evaluating and comparing test plans. These test-planning methods are illustrated with examples.

We implement here a fast and memory-sparing probabilistic top k selection algorithm on the GPU. The algorithm proceeds via an iterative probabilistic guess-and-check process on pivots for a three-way partition. When the guess is correct, the problem is reduced to selection on a much smaller set. This probabilistic algorithm always gives a correct result and always terminates. Las Vegas algorithms of this kind are a form of stochastic optimization and can be well suited to more general parallel processors with limited amounts of fast memory.

Orthogonal arrays are frequently used in industrial experi- ments for quality and productivity improvement. Due to run- size constraints and level combinations, an orthogonal array may not exist, in which case a nearly-orthogonal array can be used. Orthogonal and nearly-orthogonal arrays can be diffi- cult to find. This poster will introduce a new algorithm for the construction of orthogonal arrays

As computer simulations continue to grow in size and complexity, they provide a particularly challenging example of big data. Many application areas are moving toward exascale (i.e. 10 18 FLOPS, or FLoating-point Operations Per Second). Analyzing these simulations is difficult because their output may exceed both the storage capacity and the bandwidth required for transfer to storage. One approach is to embed some level of analysis in the simulation while the simulation is running, often called in situ analysis. This paper describes an online in situ method for approximating a complex simulation using piecewise linear fitting. Our immediate goal is to identify important time steps of the simulation. We then use those time steps and the linear fits both to significantly reduce the data transfer and storage requirements and to facilitate post processing and reconstruction of the simulation. We illustrate the method using an example that tracks the development of evolving simulation behavior by monitoring various aspects of the simulation over time.

Repeated measures degradation studies are used to assess product or component reliability when there are few or even no failures expected during a study. Such studies are used to assess the shelf life of materials and products. We show how to evaluate the properties of proposed test plans needed to identify statistically efficient tests. We consider test plans for applications where parameters related to the degradation distribution or the lifetime distribution are to be estimated. We use the approximate large-sample variance-covariance matrix of the parameters of a mixed effects linear regression model for repeated measures degradation data to assess the effect of sample size (number of units and number of measurements within the units) on estimation precision of both degradation and failure-time distribution quantiles. We also illustrate the complementary use of simulation-based methods for evaluating and comparing test plans. These test-planning methods are illustrated with examples.

As computer simulations continue to grow in size and complexity, they present a particularly challenging class of big data problems. Many application areas are moving toward exascale computing systems, systems that perform 10 18 FLOPS (FLoatingpoint Operations Per Second)-a billion billion calculations per second. Simulations

(QPRC) and the Spring Research Conference (SRC) held every 3 to 4 years. The QPRC is an annual conference (held since 1984) sponsored by the Quality and Productivity Section of the American Statistical Association (ASA). Its mission is to improve the quality of products and services and the productivity of industries by stimulating the research and development of better statistical methods and to identify new application areas where statistics can have a significant impact. The SRC is also an annual conference co-sponsored by the Institute of Mathematical Statistics and the Section on Physical and Engineering Sciences of the ASA. Its goal is to promote cross-disciplinary research in statistical methods for engineering, science, and technology. This issue contains five papers. The first paper by Kimberly F. Sellers, Sharad Borle, and Galit Shmueli gives a survey of methods and applications related to a new model for analyzing count data called the Com-Poisson Model. Two other discussion papers will help the readers to gain better understanding of this approach: one is by Ron S. Kenett and another by Dominique Lord and Seth Guikema. The paper by Jeffrey Boone and Subhabrata Chakraborti introduces new types of multivariate nonparametric control charts. The paper by David H. Collins and Aparna V. Huzurbazar introduces a new theoretical framework for developing hierarchical models for predicting health of complex systems. The paper by Theodore T. Allen and Hui Xiong proposes a new method for Pareto charting that is based on freestyle text analysis. Finally, the paper by Douglas P. Wiens and Pengfei Li introduces a method for robustification of a dose-response study. We are confident that the ASMBI readers will appreciate both the theoretical and applied aspects of the presented works. On behalf of the Program Committee of JRC 2010 and ASMBI, we would like to express our deepest appreciation to the authors, discussants, editors, and referees whose patience and dedication made this issue happen.

The availability of sophisticated modern physics codes has greatly extended the ability of domain scientists to understand the processes underlying their observations of complicated processes, but it has also introduced the curse of dimensionality via the many user-set parameters available to tune. Many of these parameters are naturally expressed as functional data, such as initial temperature distributions, equations of state, and controls. Thus, when attempting to find parameters that match observed data, being able to navigate parameter-space becomes highly non-trivial, especially considering that accurate simulations can be expensive both in terms of time and money. Existing solutions include batch-parallel simulations, high-dimensional, derivative-free optimization, and expert guessing, all of which make some contribution to solving the problem but do not completely resolve the issue. In this work, we explore the possibility of coupling together all three of the techniques just described by designing user-guided, batch-parallel optimization schemes. Our motivating example is a neutron diffusion partial differential equation where the time-varying multiplication factor serves as the unknown control parameter to be learned. We find that a simple, batch-parallelizable, random-walk scheme is able to make some progress on the problem but does not by itself produce satisfactory results. After reducing the dimensionality of the problem using functional principal component analysis (fPCA), we are able to track the progress of the solver in a visually simple way as well as viewing the associated principle components. This allows a human to make reasonable guesses about which points in the state space the random walker should try next. Thus, by combining the random walker's ability to find descent directions with the human's understanding of the underlying physics, it is possible to use expensive simulations more efficiently and more quickly arrive at the desired parameter set.

Los Alamos National Laboratory, an affirmative action/equal opportunity employer, is operated by the University of California for the U.S. Department of Energy under contract W-7405-ENG-36. By acceptance of this article, the publisher recognizes that the U.S. Government retains a nonexclusive, royalty-free license to publish or reproduce the published form of this contribution, or to allow others to do so, for U.S. Government purposes. The Los Alamos National Laboratory requests that the publisher identify this article as work performed under the auspices of the U.S. Department of Energy.